8 citations · 23 across the 5 of their papers we have counts for
5 papers
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
Sayak Mukherjee, Renke Huang, Qiuhua Huang +2
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive select…
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operati…
Parameters Calibration for Power Grid Stability Models using Deep Learning Methods
Renke Huang, Rui Fan, Tianzhixi Yin +2
This paper presents a novel parameter calibration approach for power system stability models using automatic data generation and advanced deep learning technology. A PMU-measuremen…
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection
Rui Fan, Tianzhixi Yin
This letter presents a novel high impedance fault (HIF) detection approach using a convolutional neural network (CNN). Compared to traditional artificial neural networks, a CNN off…